1,721,048 research outputs found
Intelligent data-centric critical systems: Security and resilience key challenges
No full textIntelligent data-centric critical systems are considered large-scale, spatially distributed, complex systems, composed by a huge number of heterogeneous physical hardware and software components. Such systems are not isolated, but interconnected through complex network infrastructures and interdependent at multiple levels. In modern society the continuous operation of these critical systems is pivotal for providing essential public utilities and services across national and international boundaries. Malicious attacks and deliberate system failures may produce significant effects perceivable on a regional or national scale. This poses new implications and challenges to systems security engineering. Therefore this book explores current advances in the field and disseminates recent research efforts in the security and resilience of intelligent data-centric critical systems. Its goal is to support innovations in this area and feature the latest advances and directions in this amazing scenario by exploring the potential of new architectures, applications and services, as well as understanding their weaknesses and the most common threats against them. It presents approaches, techniques, and tools to detect and prevent malicious behaviors and attacks against such complex systems and communication networks
Cyberspace Safety and Security - 10th International Symposium, CSS 2018, Amalfi, Italy, October 29-31, 2018, Proceedings.
No full textA knowledge-based platform for Big Data analytics based on publish/subscribe services and stream processing
Full text linkBig Data analytics is considered an imperative aspect to be further improved in order to increase the operating margin of both public and private enterprises, and represents the next frontier for their innovation, competition, and productivity. Big Data are typically produced in different sectors of the above organizations, often geographically distributed throughout the world, and are characterized by a large size and variety. Therefore, there is a strong need for platforms handling larger and larger amounts of data in contexts characterized by complex event processing systems and multiple heterogeneous sources, dealing with the various issues related to efficiently disseminating, collecting and analyzing them in a fully distributed way. In such a scenario, this work proposes a way to overcome two fundamental issues: data heterogeneity and advanced processing capabilities. We present a knowledge-based solution for Big Data analytics, which consists in applying automatic schema mapping to face with data heterogeneity, as well as ontology extraction and semantic inference to support innovative processing. Such a solution, based on the publish/subscribe paradigm, has been evaluated within the context of a simple experimental proof-of-concept in order to determine its performance and effectiveness
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Bug Localization in Test-Driven Development
Full text linkSoftware development teams that use agile methodologies are increasingly adopting the test-driven development practice (TDD). TDD allows to produce software by iterative and incremental work cycle, and with a strict control over the process, favouring an early detection of bugs. However, when applied to large and complex systems, TDD benefits are not so obvious; manually locating and fixing bugs introduced during the iterative development steps is a nontrivial task. In such systems, the propagation chains following the bugs activation can be unacceptably long and intricate, and the size of the code to be analyzed is often too large. In this paper, a bug localization technique specifically tailored to TDD is presented. The technique is embedded in the TDD cycle, and it aims to improve developers' ability to locate bugs as soon as possible. It is implemented in a tool and experimentally evaluated on newly developed Java programs.</jats:p
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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